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authorhelma@in-silico.ch <helma@in-silico.ch>2018-10-09 19:34:29 +0200
committerhelma@in-silico.ch <helma@in-silico.ch>2018-10-09 19:34:29 +0200
commit3f793a7be36355f7a14d0fcb4198715124b4c2b9 (patch)
tree12a28e9594a34734d0b899aec3588e46a28df3dc
parentbdc6b5b40437896384561d74a510560e9e592364 (diff)
skip rf classification tasks
-rw-r--r--test/model-classification.rb75
1 files changed, 32 insertions, 43 deletions
diff --git a/test/model-classification.rb b/test/model-classification.rb
index 232ee3f..7751bba 100644
--- a/test/model-classification.rb
+++ b/test/model-classification.rb
@@ -2,42 +2,6 @@ require_relative "setup.rb"
class LazarClassificationTest < MiniTest::Test
- def test_carcinogenicity_rf_classification
- skip "Caret rf may run into a (endless?) loop for some compounds."
- dataset = Dataset.from_csv_file "#{DATA_DIR}/multi_cell_call.csv"
- algorithms = {
- :prediction => {
- :method => "Algorithm::Caret.rf",
- },
- }
- model = Model::Lazar.create training_dataset: dataset, algorithms: algorithms
- substance = Compound.from_smiles "[O-]S(=O)(=O)[O-].[Mn+2].O"
- prediction = model.predict substance
- p prediction
-
- end
-
- def test_rf_classification
- skip "Caret rf may run into a (endless?) loop for some compounds."
- algorithms = {
- :prediction => {
- :method => "Algorithm::Caret.rf",
- },
- }
- training_dataset = Dataset.from_sdf_file File.join(DATA_DIR,"cas_4337.sdf")
- model = Model::Lazar.create training_dataset: training_dataset, algorithms: algorithms
- #p model.id.to_s
- #model = Model::Lazar.find "5bbb4c0cca626909f6c8a924"
- assert_kind_of Model::LazarClassification, model
- assert_equal algorithms[:prediction][:method], model.algorithms["prediction"]["method"]
- substance = Compound.from_smiles "Clc1ccc(cc1)C(=O)c1ccc(cc1)OC(C(=O)O)(C)C"
- prediction = model.predict substance
- assert_equal 51, prediction[:neighbors].size
- assert_equal "nonmutagen", prediction[:value]
- assert_equal 0.1, prediction[:probabilities]["mutagen"].round(1)
- assert_equal 0.9, prediction[:probabilities]["nonmutagen"].round(1)
- end
-
def test_classification_default
algorithms = {
:descriptors => {
@@ -130,15 +94,40 @@ class LazarClassificationTest < MiniTest::Test
training_dataset.delete
end
- def test_caret_classification
- skip
+ def test_carcinogenicity_rf_classification
+ skip "Caret rf may run into a (endless?) loop for some compounds."
+ dataset = Dataset.from_csv_file "#{DATA_DIR}/multi_cell_call.csv"
+ algorithms = {
+ :prediction => {
+ :method => "Algorithm::Caret.rf",
+ },
+ }
+ model = Model::Lazar.create training_dataset: dataset, algorithms: algorithms
+ substance = Compound.from_smiles "[O-]S(=O)(=O)[O-].[Mn+2].O"
+ prediction = model.predict substance
+ p prediction
+
end
- def test_fingerprint_chisq_feature_selection
- skip
+ def test_rf_classification
+ skip "Caret rf may run into a (endless?) loop for some compounds."
+ algorithms = {
+ :prediction => {
+ :method => "Algorithm::Caret.rf",
+ },
+ }
+ training_dataset = Dataset.from_sdf_file File.join(DATA_DIR,"cas_4337.sdf")
+ model = Model::Lazar.create training_dataset: training_dataset, algorithms: algorithms
+ #p model.id.to_s
+ #model = Model::Lazar.find "5bbb4c0cca626909f6c8a924"
+ assert_kind_of Model::LazarClassification, model
+ assert_equal algorithms[:prediction][:method], model.algorithms["prediction"]["method"]
+ substance = Compound.from_smiles "Clc1ccc(cc1)C(=O)c1ccc(cc1)OC(C(=O)O)(C)C"
+ prediction = model.predict substance
+ assert_equal 51, prediction[:neighbors].size
+ assert_equal "nonmutagen", prediction[:value]
+ assert_equal 0.1, prediction[:probabilities]["mutagen"].round(1)
+ assert_equal 0.9, prediction[:probabilities]["nonmutagen"].round(1)
end
- def test_physchem_classification
- skip
- end
end